Wave energy resources: Wave climate and exploitation

Portilla‐Yandún, Jesús; Sosa, Jeison; Cavaleri, Luigi

doi:10.1016/j.renene.2013.02.032

Cited by 71 publications

(38 citation statements)

References 17 publications

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“…The strong seasonality observed in the wave power is similar to the variations observed in significant wave height along the eastern Arabian Sea (Shanas and Sanil Kumar, 2014;Anoop et al, 2015). Seasonal variability in wave parameters is observed in most of the oceans (Portilla et al, 2013;McArthur and Brekken, 2010;Rusu, 2014;Rusu and Onea, 2015). Even though high seasonal variability of wave power (1-19.7 kW m −1 ) is observed in the study area, the variability is less than the seasonal variability observed in the North Atlantic.…”

Section: Discussionsupporting

confidence: 74%

Spatial and temporal variations of wave energy in the nearshore waters of the central west coast of India

Amrutha

Kumar

2016

Ann. Geophys.

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Abstract. Assessment of wave power potential at different water depths and time is required for identifying a wave power plant location. This study examines the variation in wave power off the central west coast of India at water depths of 30, 9 and 5 m based on waverider buoy measured wave data. The study shows a significant reduction ( ∼ 10 to 27 %) in wave power at 9 m water depth compared to 30 m and the wave power available at 5 m water depth is 20 to 23 % less than that at 9 m. At 9 m depth, the seasonal mean value of the wave power varied from 1.6 kW m −1 in the post-monsoon period (ONDJ) to 15.2 kW m −1 in the Indian summer monsoon (JJAS) period. During the Indian summer monsoon period, the variation of wave power in a day is up to 32 kW m −1 . At 9 m water depth, the mean annual wave power is 6 kW m −1 and interannual variations up to 19.3 % are observed during 2009-2014. High wave energy (> 20 kW m −1 ) at the study area is essentially from the directional sector 245-270 • and also 75 % of the total annual wave energy is from this narrow directional sector, which is advantageous while aligning the wave energy converter.

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Section: Discussionsupporting

confidence: 74%

Spatial and temporal variations of wave energy in the nearshore waters of the central west coast of India

Amrutha

Kumar

2016

Ann. Geophys.

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“…Algunos autores han estudiado el clima del oleaje cerca de la zona de estudio (e.g. Portilla, Sosaa & Cavaleri, 2013) y la variabilidad de la cota de inundación (Agudelo, González & Medina, 2004). Este estudio describió las condiciones de oleaje para isla Gorgona, estableciendo que en los trimestres DEF y MAM se presentó un oleaje con menor influencia de los vientos de la zona, en donde predominó el oleaje de fondo, mientras que en los trimestres JJA y SON se presentó una influencia directa del chorro de viento del Chocó.…”

Section: Discussionunclassified

Hidrodinámica aplicada a la gestión y la conservación de ecosistemas marinos y costeros: Isla Gorgona, Océano Pacífico Colombiano

Osorio

Peláez-Zapata

Guerrero-Gallego

et al. 2014

RBT

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Hydrodynamics applied to the management and conservation of marine and coastal ecosystems: Gorgona Island, Colombian Pacific Ocean. Playa Palmeras (Gorgona National Park) is one of the most important sites for sea turtle nesting in South and Central America. Because of the morphological processes affecting the beach, the turtle nests are increasingly exposed to the impact of waves and tides, threatening conservation. A study was made to determine the hydrodynamical processes of the zone adjacent to Playa Palmeras, which affects directly the morphodynamical behavior of the beach and thus the preservation of the nests. Field measurements and numerical modeling were used to obtain the general circulation patterns and thermohaline structure behavior in the area in order to determine the spatial and temporal variability of waves and its effects on the beach. A marked seasonality was found, both in the waves and the currents, influenced mainly by the meridional displacement of the iTCZ (inter-Tropical Convergence Zone) and an interannual variability of the waves, associated with ENSO (El Niño Southern Oscillation). The flooding levels of the beach were determined and flooding probability maps were made, where safer sites to locate the turtle nests could be identified. These maps serve the officials responsible of monitoring the turtles as a tool to take decisions on moving the nests, since they have flood risk information for any point on the beach. The results show that the middle zone north of the beach is the one with the lowest risk of flooding, therefore the most appropriate zone to relocate nests that are in high risk areas. Rev. Biol. Trop. 62 (Suppl. 1): 133-147. Epub 2014 February 01.

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“…A relatively detailed analysis of the spectral wave conditions of the two considered areas can be found in Portilla et al . []. For the sake of clarity of the overall procedure, it is convenient to provide here a brief summary of the main spectral wave systems for which the error covariance is to be estimated.…”

Section: Long‐term 2‐d Spectral Statisticsmentioning

confidence: 99%

“…From these two, the statistics of all the other spectral parameters (significant wave height, mean wave period, mean direction, among others) for each wave system can be derived. A relatively detailed analysis of the spectral wave conditions of the two considered areas can be found in Portilla et al [2013Portilla et al [ , 2015. For the sake of clarity of the overall procedure, Journal of Geophysical Research: Oceans…”

Section: Long-term 2-d Spectral Statisticsmentioning

confidence: 99%

On the specification of background errors for wave data assimilation systems

Portilla‐Yandún

Cavaleri

2016

JGR Oceans

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In this paper, a new methodology is proposed for the computation of Background Errors in wave data assimilation systems. Background errors define the spatial influence of an observation in the model domain. Since at present the directional wave spectrum is the fundamental variable of both state‐of‐the‐art numerical models and most modern instrumentation, this is at the core of the proposed methodology. The advantage of the spectral approach is that the wave spectrum contains detailed information of the different wave systems and physical processes at work (e.g., wind‐sea or swells). These systems have different origins and may be driven by different mechanisms, having therefore different spatial structures, length scales, and sensitivity to local wind conditions. The presented method enables making consistent and specific corrections to each component of the spectrum, in time and space. The innovations presented here require an integral look at the data assimilation algorithm for which a suitable scheme is also proposed. Examples of computed background errors are presented for shelf and oceanic basins showing the spatial structures of the different wave systems active in these areas.

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Wave energy resources: Wave climate and exploitation

Cited by 71 publications

References 17 publications

Spatial and temporal variations of wave energy in the nearshore waters of the central west coast of India

Spatial and temporal variations of wave energy in the nearshore waters of the central west coast of India

Hidrodinámica aplicada a la gestión y la conservación de ecosistemas marinos y costeros: Isla Gorgona, Océano Pacífico Colombiano

On the specification of background errors for wave data assimilation systems

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